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DLO@Scale: A Large-scale Meta Dataset for Learning Non-rigid Object Pushing Dynamics
Robert Gieselmann · Danica Kragic · Florian T. Pokorny · Alberta Longhini

Tue Dec 14 11:10 AM -- 11:12 AM (PST) @
in Physical Reasoning and Inductive Biases for the Real World »

The ability to quickly understand our physical environment and make predictions about interacting objects is fundamental to us humans. To equip artificial agents with similar reasoning capabilities, machine learning can be used to approximate the underlying state dynamics of a system. In this regard, deep learning has gained much popularity yet relying on the availability of large-enough datasets. In this work, we present DLO@Scale, a new dataset for studying future state prediction in the context of multi-body deformable linear object pushing. We provide a large collection of 100 million simulated physical interactions enabling thorough statistical analysis and algorithmic benchmarks. Our data captures complex mechanical phenomena such as elasticity, plastic deformation and friction. An important aspect is the large variation of the physical parameters making it also suitable for testing meta learning algorithms. We describe DLO@Scale in detail and present a first empirical evaluation using neural network baselines.

Author Information

Robert Gieselmann (KTH Royal Institute of Technology)
Danica Kragic (KTH Royal Institute of Technology)
Florian T. Pokorny (KTH Royal Institute of Technology)
Alberta Longhini (KTH - Royal Institute of Technology)

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